Overview

Brought to you by YData

Dataset statistics

Number of variables71
Number of observations8510
Missing cells59806
Missing cells (%)9.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory18.7 MiB
Average record size in memory2.3 KiB

Variable types

Categorical39
DateTime8
Numeric16
Text6
Unsupported2

Alerts

GP_DESMOVI has constant value "2" Constant
va_sispro has constant value "1" Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
COD_EVE is highly imbalanced (61.9%) Imbalance
UNI_MED is highly imbalanced (84.3%) Imbalance
nombre_nacionalidad is highly imbalanced (94.0%) Imbalance
COD_PAIS_O is highly imbalanced (97.2%) Imbalance
nom_grupo is highly imbalanced (96.4%) Imbalance
GP_DISCAPA is highly imbalanced (96.7%) Imbalance
GP_DESPLAZ is highly imbalanced (97.8%) Imbalance
GP_MIGRANT is highly imbalanced (87.6%) Imbalance
GP_CARCELA is highly imbalanced (99.0%) Imbalance
GP_GESTAN is highly imbalanced (88.8%) Imbalance
sem_ges is highly imbalanced (96.4%) Imbalance
GP_INDIGEN is highly imbalanced (99.4%) Imbalance
GP_POBICFB is highly imbalanced (99.5%) Imbalance
GP_MAD_COM is highly imbalanced (99.7%) Imbalance
GP_PSIQUIA is highly imbalanced (99.4%) Imbalance
GP_VIC_VIO is highly imbalanced (98.7%) Imbalance
GP_OTROS is highly imbalanced (76.9%) Imbalance
fuente is highly imbalanced (78.1%) Imbalance
PAC_HOS is highly imbalanced (75.9%) Imbalance
CON_FIN is highly imbalanced (59.7%) Imbalance
confirmados is highly imbalanced (68.8%) Imbalance
Estado_final_de_caso is highly imbalanced (79.4%) Imbalance
nom_est_f_caso is highly imbalanced (79.4%) Imbalance
Pais_ocurrencia is highly imbalanced (97.2%) Imbalance
Nombre_evento is highly imbalanced (61.9%) Imbalance
Pais_residencia is highly imbalanced (97.6%) Imbalance
COD_ASE has 231 (2.7%) missing values Missing
GRU_POB has 8510 (100.0%) missing values Missing
FEC_HOS has 338 (4.0%) missing values Missing
FEC_DEF has 7841 (92.1%) missing values Missing
FECHA_NTO has 1720 (20.2%) missing values Missing
CER_DEF has 7829 (92.0%) missing values Missing
CBMTE has 7825 (92.0%) missing values Missing
FM_FUERZA has 8500 (99.9%) missing values Missing
FM_UNIDAD has 8500 (99.9%) missing values Missing
FM_GRADO has 8500 (99.9%) missing values Missing
GRU_POB is an unsupported type, check if it needs cleaning or further analysis Unsupported
FM_GRADO is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-10-08 14:47:49.185086
Analysis finished2025-10-08 14:48:31.579695
Duration42.39 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

COD_EVE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size565.1 KiB
220
7879 
580
 
631

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25530
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row220
2nd row220
3rd row220
4th row220
5th row220

Common Values

ValueCountFrequency (%)
220 7879
92.6%
580 631
 
7.4%

Length

2025-10-08T09:48:31.830206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:31.955211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
220 7879
92.6%
580 631
 
7.4%

Most occurring characters

ValueCountFrequency (%)
2 15758
61.7%
0 8510
33.3%
5 631
 
2.5%
8 631
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25530
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15758
61.7%
0 8510
33.3%
5 631
 
2.5%
8 631
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25530
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15758
61.7%
0 8510
33.3%
5 631
 
2.5%
8 631
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15758
61.7%
0 8510
33.3%
5 631
 
2.5%
8 631
 
2.5%

FEC_NOT
Date

Distinct1668
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size133.0 KiB
Minimum2019-12-30 00:00:00
Maximum2025-03-15 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:32.080207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:32.223299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SEMANA
Real number (ℝ)

Distinct53
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.577673
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:32.379548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q339
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.024664
Coefficient of variation (CV)0.56531149
Kurtosis-1.1554345
Mean26.577673
Median Absolute Deviation (MAD)13
Skewness0.0044193485
Sum226176
Variance225.74053
MonotonicityNot monotonic
2025-10-08T09:48:32.535799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 195
 
2.3%
23 195
 
2.3%
29 195
 
2.3%
27 195
 
2.3%
5 193
 
2.3%
28 193
 
2.3%
31 193
 
2.3%
20 190
 
2.2%
1 189
 
2.2%
32 189
 
2.2%
Other values (43) 6583
77.4%
ValueCountFrequency (%)
1 189
2.2%
2 147
1.7%
3 164
1.9%
4 165
1.9%
5 193
2.3%
6 165
1.9%
7 166
2.0%
8 145
1.7%
9 157
1.8%
10 151
1.8%
ValueCountFrequency (%)
53 13
 
0.2%
52 175
2.1%
51 169
2.0%
50 195
2.3%
49 187
2.2%
48 151
1.8%
47 133
1.6%
46 179
2.1%
45 175
2.1%
44 166
2.0%

ANO
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size573.4 KiB
2024
3300 
2023
1839 
2022
1425 
2021
1014 
2020
932 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters34040
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2024 3300
38.8%
2023 1839
21.6%
2022 1425
16.7%
2021 1014
 
11.9%
2020 932
 
11.0%

Length

2025-10-08T09:48:32.660799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:32.774839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 3300
38.8%
2023 1839
21.6%
2022 1425
16.7%
2021 1014
 
11.9%
2020 932
 
11.0%

Most occurring characters

ValueCountFrequency (%)
2 18445
54.2%
0 9442
27.7%
4 3300
 
9.7%
3 1839
 
5.4%
1 1014
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34040
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18445
54.2%
0 9442
27.7%
4 3300
 
9.7%
3 1839
 
5.4%
1 1014
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18445
54.2%
0 9442
27.7%
4 3300
 
9.7%
3 1839
 
5.4%
1 1014
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18445
54.2%
0 9442
27.7%
4 3300
 
9.7%
3 1839
 
5.4%
1 1014
 
3.0%

COD_PRE
Real number (ℝ)

Distinct659
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5095999 × 109
Minimum5.0010115 × 108
Maximum9.9773 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:32.993559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.0010115 × 108
5-th percentile8.0010079 × 108
Q11.9431763 × 109
median4.4430008 × 109
Q37.3001009 × 109
95-th percentile7.6323972 × 109
Maximum9.9773 × 109
Range9.4771989 × 109
Interquartile range (IQR)5.3569246 × 109

Descriptive statistics

Standard deviation2.6518612 × 109
Coefficient of variation (CV)0.58804799
Kurtosis-1.482303
Mean4.5095999 × 109
Median Absolute Deviation (MAD)2.5784996 × 109
Skewness-0.11884565
Sum3.8376696 × 1013
Variance7.0323677 × 1018
MonotonicityNot monotonic
2025-10-08T09:48:33.322487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4100100562 341
 
4.0%
7600102541 338
 
4.0%
800100789 259
 
3.0%
4100100572 231
 
2.7%
2001100572 230
 
2.7%
7600102870 181
 
2.1%
7000101513 181
 
2.1%
4100100385 178
 
2.1%
1300100568 155
 
1.8%
7326800794 151
 
1.8%
Other values (649) 6265
73.6%
ValueCountFrequency (%)
500101150 8
 
0.1%
500101539 1
 
< 0.1%
500102092 11
0.1%
500102101 6
 
0.1%
500102104 24
0.3%
500102120 2
 
< 0.1%
500102124 7
 
0.1%
500102126 5
 
0.1%
500102144 7
 
0.1%
500102172 1
 
< 0.1%
ValueCountFrequency (%)
9977300006 1
 
< 0.1%
9952400006 1
 
< 0.1%
9900100006 4
< 0.1%
9700100001 6
0.1%
9502500003 1
 
< 0.1%
9500100001 4
< 0.1%
9434300057 2
 
< 0.1%
9400100065 3
 
< 0.1%
9400100057 4
< 0.1%
9100100019 8
0.1%

COD_SUB
Real number (ℝ)

Distinct48
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6324324
Minimum0
Maximum99
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:33.634985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile10
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2981777
Coefficient of variation (CV)2.7724084
Kurtosis56.557509
Mean2.6324324
Median Absolute Deviation (MAD)0
Skewness6.8880628
Sum22402
Variance53.263397
MonotonicityNot monotonic
2025-10-08T09:48:33.964678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 7108
83.5%
3 392
 
4.6%
2 310
 
3.6%
10 109
 
1.3%
7 69
 
0.8%
6 62
 
0.7%
38 57
 
0.7%
4 43
 
0.5%
14 40
 
0.5%
8 32
 
0.4%
Other values (38) 288
 
3.4%
ValueCountFrequency (%)
0 29
 
0.3%
1 7108
83.5%
2 310
 
3.6%
3 392
 
4.6%
4 43
 
0.5%
5 10
 
0.1%
6 62
 
0.7%
7 69
 
0.8%
8 32
 
0.4%
9 9
 
0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
83 11
0.1%
82 2
 
< 0.1%
81 6
0.1%
80 4
 
< 0.1%
70 1
 
< 0.1%
62 6
0.1%
61 2
 
< 0.1%
60 2
 
< 0.1%
57 1
 
< 0.1%

EDAD
Real number (ℝ)

Distinct98
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.718096
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:34.294196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median15
Q328
95-th percentile71
Maximum98
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.265933
Coefficient of variation (CV)0.89206123
Kurtosis1.5944298
Mean22.718096
Median Absolute Deviation (MAD)7
Skewness1.5536871
Sum193331
Variance410.70804
MonotonicityNot monotonic
2025-10-08T09:48:34.545414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 396
 
4.7%
10 388
 
4.6%
9 387
 
4.5%
14 381
 
4.5%
15 368
 
4.3%
6 368
 
4.3%
13 352
 
4.1%
8 350
 
4.1%
12 347
 
4.1%
16 335
 
3.9%
Other values (88) 4838
56.9%
ValueCountFrequency (%)
1 73
 
0.9%
2 90
 
1.1%
3 134
 
1.6%
4 185
2.2%
5 271
3.2%
6 368
4.3%
7 396
4.7%
8 350
4.1%
9 387
4.5%
10 388
4.6%
ValueCountFrequency (%)
98 1
 
< 0.1%
97 1
 
< 0.1%
96 2
 
< 0.1%
95 2
 
< 0.1%
94 7
0.1%
93 5
0.1%
92 2
 
< 0.1%
91 4
< 0.1%
90 9
0.1%
89 7
0.1%

UNI_MED
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
8169 
2
 
333
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8169
96.0%
2 333
 
3.9%
3 8
 
0.1%

Length

2025-10-08T09:48:34.670410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:34.764164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8169
96.0%
2 333
 
3.9%
3 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 8169
96.0%
2 333
 
3.9%
3 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8169
96.0%
2 333
 
3.9%
3 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8169
96.0%
2 333
 
3.9%
3 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8169
96.0%
2 333
 
3.9%
3 8
 
0.1%

nacionalidad
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.32315
Minimum68
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:34.876051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile170
Q1170
median170
Q3170
95-th percentile170
Maximum862
Range794
Interquartile range (IQR)0

Descriptive statistics

Standard deviation110.89979
Coefficient of variation (CV)0.58888027
Kurtosis32.75358
Mean188.32315
Median Absolute Deviation (MAD)0
Skewness5.8887962
Sum1602630
Variance12298.763
MonotonicityNot monotonic
2025-10-08T09:48:34.985461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
170 8273
97.2%
862 221
 
2.6%
604 3
 
< 0.1%
218 3
 
< 0.1%
68 3
 
< 0.1%
180 2
 
< 0.1%
840 2
 
< 0.1%
528 1
 
< 0.1%
100 1
 
< 0.1%
380 1
 
< 0.1%
ValueCountFrequency (%)
68 3
 
< 0.1%
100 1
 
< 0.1%
170 8273
97.2%
180 2
 
< 0.1%
218 3
 
< 0.1%
380 1
 
< 0.1%
528 1
 
< 0.1%
604 3
 
< 0.1%
840 2
 
< 0.1%
862 221
 
2.6%
ValueCountFrequency (%)
862 221
 
2.6%
840 2
 
< 0.1%
604 3
 
< 0.1%
528 1
 
< 0.1%
380 1
 
< 0.1%
218 3
 
< 0.1%
180 2
 
< 0.1%
170 8273
97.2%
100 1
 
< 0.1%
68 3
 
< 0.1%

nombre_nacionalidad
Categorical

Imbalance 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
COLOMBIA
8273 
VENEZUELA
 
221
PERÚ
 
3
ECUADOR
 
3
BOLIVIA
 
3
Other values (5)
 
7

Length

Max length100
Median length100
Mean length100
Min length100

Characters and Unicode

Total characters851000
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 8273
97.2%
VENEZUELA 221
 
2.6%
PERÚ 3
 
< 0.1%
ECUADOR 3
 
< 0.1%
BOLIVIA 3
 
< 0.1%
REPUBLICA DEMOCRÁTICA DEL CONGO 2
 
< 0.1%
ESTADOS UNIDOS DE AMÉRICA 2
 
< 0.1%
PAÍSES BAJOS 1
 
< 0.1%
BULGARIA 1
 
< 0.1%
ITALIA 1
 
< 0.1%

Length

2025-10-08T09:48:35.110460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:35.251089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
colombia 8273
97.1%
venezuela 221
 
2.6%
perú 3
 
< 0.1%
ecuador 3
 
< 0.1%
bolivia 3
 
< 0.1%
estados 2
 
< 0.1%
américa 2
 
< 0.1%
de 2
 
< 0.1%
unidos 2
 
< 0.1%
congo 2
 
< 0.1%
Other values (7) 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
782648
92.0%
O 16563
 
1.9%
A 8516
 
1.0%
L 8503
 
1.0%
I 8290
 
1.0%
C 8286
 
1.0%
B 8280
 
1.0%
M 8277
 
1.0%
E 680
 
0.1%
U 229
 
< 0.1%
Other values (14) 728
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 782648
92.0%
Uppercase Letter 68352
 
8.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 16563
24.2%
A 8516
12.5%
L 8503
12.4%
I 8290
12.1%
C 8286
12.1%
B 8280
12.1%
M 8277
12.1%
E 680
 
1.0%
U 229
 
0.3%
N 225
 
0.3%
Other values (13) 503
 
0.7%
Space Separator
ValueCountFrequency (%)
782648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 782648
92.0%
Latin 68352
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 16563
24.2%
A 8516
12.5%
L 8503
12.4%
I 8290
12.1%
C 8286
12.1%
B 8280
12.1%
M 8277
12.1%
E 680
 
1.0%
U 229
 
0.3%
N 225
 
0.3%
Other values (13) 503
 
0.7%
Common
ValueCountFrequency (%)
782648
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 850992
> 99.9%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
782648
92.0%
O 16563
 
1.9%
A 8516
 
1.0%
L 8503
 
1.0%
I 8290
 
1.0%
C 8286
 
1.0%
B 8280
 
1.0%
M 8277
 
1.0%
E 680
 
0.1%
U 229
 
< 0.1%
Other values (10) 720
 
0.1%
None
ValueCountFrequency (%)
Ú 3
37.5%
Á 2
25.0%
É 2
25.0%
Í 1
 
12.5%

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
M
4289 
F
4221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 4289
50.4%
F 4221
49.6%

Length

2025-10-08T09:48:35.425560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:35.534928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 4289
50.4%
f 4221
49.6%

Most occurring characters

ValueCountFrequency (%)
M 4289
50.4%
F 4221
49.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8510
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 4289
50.4%
F 4221
49.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 8510
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 4289
50.4%
F 4221
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 4289
50.4%
F 4221
49.6%

COD_PAIS_O
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size565.1 KiB
170
8447 
862
 
60
214
 
1
604
 
1
68
 
1

Length

Max length3
Median length3
Mean length2.9998825
Min length2

Characters and Unicode

Total characters25529
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row170
2nd row170
3rd row170
4th row170
5th row170

Common Values

ValueCountFrequency (%)
170 8447
99.3%
862 60
 
0.7%
214 1
 
< 0.1%
604 1
 
< 0.1%
68 1
 
< 0.1%

Length

2025-10-08T09:48:35.644313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:35.753689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
170 8447
99.3%
862 60
 
0.7%
214 1
 
< 0.1%
604 1
 
< 0.1%
68 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 8448
33.1%
0 8448
33.1%
7 8447
33.1%
6 62
 
0.2%
8 61
 
0.2%
2 61
 
0.2%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25529
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8448
33.1%
0 8448
33.1%
7 8447
33.1%
6 62
 
0.2%
8 61
 
0.2%
2 61
 
0.2%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8448
33.1%
0 8448
33.1%
7 8447
33.1%
6 62
 
0.2%
8 61
 
0.2%
2 61
 
0.2%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8448
33.1%
0 8448
33.1%
7 8447
33.1%
6 62
 
0.2%
8 61
 
0.2%
2 61
 
0.2%
4 2
 
< 0.1%

COD_DPTO_O
Real number (ℝ)

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.468625
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:35.910794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median44
Q373
95-th percentile76
Maximum99
Range98
Interquartile range (IQR)55

Descriptive statistics

Standard deviation26.955082
Coefficient of variation (CV)0.60615956
Kurtosis-1.493614
Mean44.468625
Median Absolute Deviation (MAD)29
Skewness-0.075075307
Sum378428
Variance726.57646
MonotonicityNot monotonic
2025-10-08T09:48:36.083356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
76 1319
15.5%
41 922
10.8%
13 871
10.2%
8 782
 
9.2%
73 585
 
6.9%
68 521
 
6.1%
20 409
 
4.8%
54 374
 
4.4%
5 345
 
4.1%
70 343
 
4.0%
Other values (23) 2039
24.0%
ValueCountFrequency (%)
1 63
 
0.7%
5 345
 
4.1%
8 782
9.2%
13 871
10.2%
15 22
 
0.3%
17 24
 
0.3%
18 112
 
1.3%
19 153
 
1.8%
20 409
4.8%
23 187
 
2.2%
ValueCountFrequency (%)
99 8
 
0.1%
97 7
 
0.1%
95 17
 
0.2%
94 8
 
0.1%
91 12
 
0.1%
88 5
 
0.1%
86 81
 
1.0%
85 139
 
1.6%
81 60
 
0.7%
76 1319
15.5%

COD_MUN_O
Real number (ℝ)

Distinct395
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.2604
Minimum1
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:36.395853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median168
Q3547
95-th percentile834
Maximum980
Range979
Interquartile range (IQR)546

Descriptive statistics

Standard deviation297.90618
Coefficient of variation (CV)1.0862165
Kurtosis-1.0608808
Mean274.2604
Median Absolute Deviation (MAD)167
Skewness0.62280871
Sum2333956
Variance88748.091
MonotonicityNot monotonic
2025-10-08T09:48:36.726687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3317
39.0%
758 160
 
1.9%
11 147
 
1.7%
551 136
 
1.6%
268 124
 
1.5%
520 120
 
1.4%
276 90
 
1.1%
573 85
 
1.0%
547 79
 
0.9%
396 74
 
0.9%
Other values (385) 4178
49.1%
ValueCountFrequency (%)
1 3317
39.0%
3 13
 
0.2%
6 42
 
0.5%
10 18
 
0.2%
11 147
 
1.7%
13 21
 
0.2%
15 2
 
< 0.1%
16 22
 
0.3%
20 27
 
0.3%
25 2
 
< 0.1%
ValueCountFrequency (%)
980 12
 
0.1%
895 13
 
0.2%
894 2
 
< 0.1%
893 6
 
0.1%
892 36
0.4%
890 8
 
0.1%
885 12
 
0.1%
878 3
 
< 0.1%
877 3
 
< 0.1%
875 10
 
0.1%

AREA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
6977 
3
905 
2
 
628

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6977
82.0%
3 905
 
10.6%
2 628
 
7.4%

Length

2025-10-08T09:48:37.026180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:37.244930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6977
82.0%
3 905
 
10.6%
2 628
 
7.4%

Most occurring characters

ValueCountFrequency (%)
1 6977
82.0%
3 905
 
10.6%
2 628
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6977
82.0%
3 905
 
10.6%
2 628
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6977
82.0%
3 905
 
10.6%
2 628
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6977
82.0%
3 905
 
10.6%
2 628
 
7.4%

OCUPACION
Real number (ℝ)

Distinct276
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60011.42
Minimum210
Maximum99999.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:37.528712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile9996
Q19998
median96220
Q399999.05
95-th percentile99999.07
Maximum99999.08
Range99789.08
Interquartile range (IQR)90001.05

Descriptive statistics

Standard deviation43475.263
Coefficient of variation (CV)0.72444982
Kurtosis-1.8880991
Mean60011.42
Median Absolute Deviation (MAD)3779.07
Skewness-0.23619705
Sum5.1069719 × 108
Variance1.8900985 × 109
MonotonicityNot monotonic
2025-10-08T09:48:37.731835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99999.05 2153
25.3%
9997 1432
16.8%
9999 1331
15.6%
99999.07 778
 
9.1%
99999.04 643
 
7.6%
99999.06 279
 
3.3%
99999.01 277
 
3.3%
9996 263
 
3.1%
96220 244
 
2.9%
99999.03 87
 
1.0%
Other values (266) 1023
12.0%
ValueCountFrequency (%)
210 1
 
< 0.1%
310 3
< 0.1%
1100 5
0.1%
1114 1
 
< 0.1%
1211 1
 
< 0.1%
1221 1
 
< 0.1%
1412 1
 
< 0.1%
2100 2
 
< 0.1%
2141 2
 
< 0.1%
2144 1
 
< 0.1%
ValueCountFrequency (%)
99999.08 2
 
< 0.1%
99999.07 778
 
9.1%
99999.06 279
 
3.3%
99999.05 2153
25.3%
99999.04 643
 
7.6%
99999.03 87
 
1.0%
99999.02 1
 
< 0.1%
99999.01 277
 
3.3%
96299 4
 
< 0.1%
96292 1
 
< 0.1%

TIP_SS
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
S
4980 
C
2933 
P
 
335
N
 
178
I
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowC
5th rowP

Common Values

ValueCountFrequency (%)
S 4980
58.5%
C 2933
34.5%
P 335
 
3.9%
N 178
 
2.1%
I 53
 
0.6%
E 31
 
0.4%

Length

2025-10-08T09:48:37.856832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:37.966207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 4980
58.5%
c 2933
34.5%
p 335
 
3.9%
n 178
 
2.1%
i 53
 
0.6%
e 31
 
0.4%

Most occurring characters

ValueCountFrequency (%)
S 4980
58.5%
C 2933
34.5%
P 335
 
3.9%
N 178
 
2.1%
I 53
 
0.6%
E 31
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8510
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4980
58.5%
C 2933
34.5%
P 335
 
3.9%
N 178
 
2.1%
I 53
 
0.6%
E 31
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8510
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4980
58.5%
C 2933
34.5%
P 335
 
3.9%
N 178
 
2.1%
I 53
 
0.6%
E 31
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 4980
58.5%
C 2933
34.5%
P 335
 
3.9%
N 178
 
2.1%
I 53
 
0.6%
E 31
 
0.4%

COD_ASE
Text

Missing 

Distinct94
Distinct (%)1.1%
Missing231
Missing (%)2.7%
Memory size583.1 KiB
2025-10-08T09:48:38.156473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9996376
Min length5

Characters and Unicode

Total characters49671
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st rowEPSS18
2nd rowESS118
3rd rowESS118
4th rowEPS016
5th rowRES002
ValueCountFrequency (%)
epss41 811
 
9.8%
ess024 586
 
7.1%
eps002 576
 
7.0%
eps037 528
 
6.4%
eps005 517
 
6.2%
ess207 501
 
6.1%
eps010 385
 
4.7%
ess062 384
 
4.6%
epss05 334
 
4.0%
ess118 289
 
3.5%
Other values (84) 3368
40.7%
2025-10-08T09:48:38.469012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 12045
24.2%
0 8223
16.6%
E 7786
15.7%
P 5456
11.0%
1 2899
 
5.8%
2 2767
 
5.6%
4 2375
 
4.8%
7 1605
 
3.2%
5 1582
 
3.2%
3 1380
 
2.8%
Other values (9) 3553
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27561
55.5%
Decimal Number 22110
44.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8223
37.2%
1 2899
 
13.1%
2 2767
 
12.5%
4 2375
 
10.7%
7 1605
 
7.3%
5 1582
 
7.2%
3 1380
 
6.2%
8 722
 
3.3%
6 525
 
2.4%
9 32
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
S 12045
43.7%
E 7786
28.3%
P 5456
19.8%
C 1131
 
4.1%
F 492
 
1.8%
R 359
 
1.3%
I 251
 
0.9%
M 37
 
0.1%
A 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 27561
55.5%
Common 22110
44.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8223
37.2%
1 2899
 
13.1%
2 2767
 
12.5%
4 2375
 
10.7%
7 1605
 
7.3%
5 1582
 
7.2%
3 1380
 
6.2%
8 722
 
3.3%
6 525
 
2.4%
9 32
 
0.1%
Latin
ValueCountFrequency (%)
S 12045
43.7%
E 7786
28.3%
P 5456
19.8%
C 1131
 
4.1%
F 492
 
1.8%
R 359
 
1.3%
I 251
 
0.9%
M 37
 
0.1%
A 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 12045
24.2%
0 8223
16.6%
E 7786
15.7%
P 5456
11.0%
1 2899
 
5.8%
2 2767
 
5.6%
4 2375
 
4.8%
7 1605
 
3.2%
5 1582
 
3.2%
3 1380
 
2.8%
Other values (9) 3553
 
7.2%

PER_ETN
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8750881
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:38.595352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69348992
Coefficient of variation (CV)0.11803907
Kurtosis41.372179
Mean5.8750881
Median Absolute Deviation (MAD)0
Skewness-6.4305025
Sum49997
Variance0.48092827
MonotonicityNot monotonic
2025-10-08T09:48:38.704727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 8093
95.1%
5 248
 
2.9%
1 148
 
1.7%
2 14
 
0.2%
3 5
 
0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
1 148
 
1.7%
2 14
 
0.2%
3 5
 
0.1%
4 2
 
< 0.1%
5 248
 
2.9%
6 8093
95.1%
ValueCountFrequency (%)
6 8093
95.1%
5 248
 
2.9%
4 2
 
< 0.1%
3 5
 
0.1%
2 14
 
0.2%
1 148
 
1.7%

GRU_POB
Unsupported

Missing  Rejected  Unsupported 

Missing8510
Missing (%)100.0%
Memory size133.0 KiB

nom_grupo
Categorical

Imbalance 

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
8364 
WAYUU
 
67
EMBERA
 
12
ZENU
 
10
AWA
 
6
Other values (28)
 
51

Length

Max length100
Median length100
Mean length100
Min length100

Characters and Unicode

Total characters851000
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
8364
98.3%
WAYUU 67
 
0.8%
EMBERA 12
 
0.1%
ZENU 10
 
0.1%
AWA 6
 
0.1%
SIKUANI 5
 
0.1%
EMBERA KATIO 4
 
< 0.1%
CUBEO 4
 
< 0.1%
KANKUAMO 4
 
< 0.1%
NASA 4
 
< 0.1%
Other values (23) 30
 
0.4%

Length

2025-10-08T09:48:38.985977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wayuu 67
42.1%
embera 19
 
11.9%
zenu 10
 
6.3%
awa 6
 
3.8%
sikuani 5
 
3.1%
katio 4
 
2.5%
cubeo 4
 
2.5%
kankuamo 4
 
2.5%
nasa 4
 
2.5%
tikuna 3
 
1.9%
Other values (25) 33
20.8%

Most occurring characters

ValueCountFrequency (%)
850153
99.9%
U 176
 
< 0.1%
A 165
 
< 0.1%
W 75
 
< 0.1%
Y 71
 
< 0.1%
E 58
 
< 0.1%
N 39
 
< 0.1%
O 36
 
< 0.1%
I 36
 
< 0.1%
B 32
 
< 0.1%
Other values (14) 159
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 850153
99.9%
Uppercase Letter 847
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 176
20.8%
A 165
19.5%
W 75
8.9%
Y 71
8.4%
E 58
 
6.8%
N 39
 
4.6%
O 36
 
4.3%
I 36
 
4.3%
B 32
 
3.8%
R 31
 
3.7%
Other values (13) 128
15.1%
Space Separator
ValueCountFrequency (%)
850153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 850153
99.9%
Latin 847
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 176
20.8%
A 165
19.5%
W 75
8.9%
Y 71
8.4%
E 58
 
6.8%
N 39
 
4.6%
O 36
 
4.3%
I 36
 
4.3%
B 32
 
3.8%
R 31
 
3.7%
Other values (13) 128
15.1%
Common
ValueCountFrequency (%)
850153
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 851000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
850153
99.9%
U 176
 
< 0.1%
A 165
 
< 0.1%
W 75
 
< 0.1%
Y 71
 
< 0.1%
E 58
 
< 0.1%
N 39
 
< 0.1%
O 36
 
< 0.1%
I 36
 
< 0.1%
B 32
 
< 0.1%
Other values (14) 159
 
< 0.1%

estrato
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size623.3 KiB
1
3894 
2
3073 
3
798 
577 
4
 
118
Other values (2)
 
50

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters85100
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 3894
45.8%
2 3073
36.1%
3 798
 
9.4%
577
 
6.8%
4 118
 
1.4%
5 34
 
0.4%
6 16
 
0.2%

Length

2025-10-08T09:48:39.097885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:39.207261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3894
49.1%
2 3073
38.7%
3 798
 
10.1%
4 118
 
1.5%
5 34
 
0.4%
6 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
77167
90.7%
1 3894
 
4.6%
2 3073
 
3.6%
3 798
 
0.9%
4 118
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 77167
90.7%
Decimal Number 7933
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3894
49.1%
2 3073
38.7%
3 798
 
10.1%
4 118
 
1.5%
5 34
 
0.4%
6 16
 
0.2%
Space Separator
ValueCountFrequency (%)
77167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
77167
90.7%
1 3894
 
4.6%
2 3073
 
3.6%
3 798
 
0.9%
4 118
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77167
90.7%
1 3894
 
4.6%
2 3073
 
3.6%
3 798
 
0.9%
4 118
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%

GP_DISCAPA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8481 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8481
99.7%
1 29
 
0.3%

Length

2025-10-08T09:48:39.347886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:39.457261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8481
99.7%
1 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 8481
99.7%
1 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8481
99.7%
1 29
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8481
99.7%
1 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8481
99.7%
1 29
 
0.3%

GP_DESPLAZ
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8492 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8492
99.8%
1 18
 
0.2%

Length

2025-10-08T09:48:39.551011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:39.664107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8492
99.8%
1 18
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 8492
99.8%
1 18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8492
99.8%
1 18
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8492
99.8%
1 18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8492
99.8%
1 18
 
0.2%

GP_MIGRANT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8365 
1
 
145

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8365
98.3%
1 145
 
1.7%

Length

2025-10-08T09:48:39.757821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:39.867196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8365
98.3%
1 145
 
1.7%

Most occurring characters

ValueCountFrequency (%)
2 8365
98.3%
1 145
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8365
98.3%
1 145
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8365
98.3%
1 145
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8365
98.3%
1 145
 
1.7%

GP_CARCELA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8503 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8503
99.9%
1 7
 
0.1%

Length

2025-10-08T09:48:39.960946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:40.070319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8503
99.9%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 8503
99.9%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8503
99.9%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8503
99.9%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8503
99.9%
1 7
 
0.1%

GP_GESTAN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8383 
1
 
127

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8383
98.5%
1 127
 
1.5%

Length

2025-10-08T09:48:40.181255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:40.274970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8383
98.5%
1 127
 
1.5%

Most occurring characters

ValueCountFrequency (%)
2 8383
98.5%
1 127
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8383
98.5%
1 127
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8383
98.5%
1 127
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8383
98.5%
1 127
 
1.5%

sem_ges
Categorical

Imbalance 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size623.3 KiB
8383 
27
 
8
36
 
7
24
 
7
26
 
7
Other values (32)
 
98

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters85100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
8383
98.5%
27 8
 
0.1%
36 7
 
0.1%
24 7
 
0.1%
26 7
 
0.1%
34 7
 
0.1%
10 6
 
0.1%
33 5
 
0.1%
31 5
 
0.1%
14 5
 
0.1%
Other values (27) 70
 
0.8%

Length

2025-10-08T09:48:40.399980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27 8
 
6.3%
36 7
 
5.5%
24 7
 
5.5%
26 7
 
5.5%
34 7
 
5.5%
10 6
 
4.7%
28 5
 
3.9%
19 5
 
3.9%
14 5
 
3.9%
31 5
 
3.9%
Other values (26) 65
51.2%

Most occurring characters

ValueCountFrequency (%)
84857
99.7%
2 57
 
0.1%
3 48
 
0.1%
1 40
 
< 0.1%
4 21
 
< 0.1%
6 20
 
< 0.1%
7 14
 
< 0.1%
9 13
 
< 0.1%
0 12
 
< 0.1%
8 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 84857
99.7%
Decimal Number 243
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 57
23.5%
3 48
19.8%
1 40
16.5%
4 21
 
8.6%
6 20
 
8.2%
7 14
 
5.8%
9 13
 
5.3%
0 12
 
4.9%
8 11
 
4.5%
5 7
 
2.9%
Space Separator
ValueCountFrequency (%)
84857
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
84857
99.7%
2 57
 
0.1%
3 48
 
0.1%
1 40
 
< 0.1%
4 21
 
< 0.1%
6 20
 
< 0.1%
7 14
 
< 0.1%
9 13
 
< 0.1%
0 12
 
< 0.1%
8 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84857
99.7%
2 57
 
0.1%
3 48
 
0.1%
1 40
 
< 0.1%
4 21
 
< 0.1%
6 20
 
< 0.1%
7 14
 
< 0.1%
9 13
 
< 0.1%
0 12
 
< 0.1%
8 11
 
< 0.1%

GP_INDIGEN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8506 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Length

2025-10-08T09:48:40.603139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:40.823975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

GP_POBICFB
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8507 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8507
> 99.9%
1 3
 
< 0.1%

Length

2025-10-08T09:48:41.042766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:41.263680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8507
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 8507
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8507
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8507
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8507
> 99.9%
1 3
 
< 0.1%

GP_MAD_COM
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8508 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8508
> 99.9%
1 2
 
< 0.1%

Length

2025-10-08T09:48:41.498047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:41.718161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8508
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 8508
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8508
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8508
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8508
> 99.9%
1 2
 
< 0.1%

GP_DESMOVI
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8510 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8510
100.0%

Length

2025-10-08T09:48:41.938206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:42.125737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8510
100.0%

Most occurring characters

ValueCountFrequency (%)
2 8510
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8510
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8510
100.0%

GP_PSIQUIA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8506 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Length

2025-10-08T09:48:42.219452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:42.315791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8506
> 99.9%
1 4
 
< 0.1%

GP_VIC_VIO
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
8500 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8500
99.9%
1 10
 
0.1%

Length

2025-10-08T09:48:42.425155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:42.534538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8500
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 8500
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8500
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8500
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8500
99.9%
1 10
 
0.1%

GP_OTROS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
8190 
2
 
320

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8190
96.2%
2 320
 
3.8%

Length

2025-10-08T09:48:42.628288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:42.737662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8190
96.2%
2 320
 
3.8%

Most occurring characters

ValueCountFrequency (%)
1 8190
96.2%
2 320
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8190
96.2%
2 320
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8190
96.2%
2 320
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8190
96.2%
2 320
 
3.8%

fuente
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
7726 
2
 
673
3
 
95
4
 
8
5
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7726
90.8%
2 673
 
7.9%
3 95
 
1.1%
4 8
 
0.1%
5 8
 
0.1%

Length

2025-10-08T09:48:42.848563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:42.973527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7726
90.8%
2 673
 
7.9%
3 95
 
1.1%
4 8
 
0.1%
5 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 7726
90.8%
2 673
 
7.9%
3 95
 
1.1%
4 8
 
0.1%
5 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7726
90.8%
2 673
 
7.9%
3 95
 
1.1%
4 8
 
0.1%
5 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7726
90.8%
2 673
 
7.9%
3 95
 
1.1%
4 8
 
0.1%
5 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7726
90.8%
2 673
 
7.9%
3 95
 
1.1%
4 8
 
0.1%
5 8
 
0.1%

COD_PAIS_R
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.52761
Minimum10
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:43.067315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile170
Q1170
median170
Q3170
95-th percentile170
Maximum862
Range852
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.512047
Coefficient of variation (CV)0.31807028
Kurtosis147.21882
Mean174.52761
Median Absolute Deviation (MAD)0
Skewness12.176775
Sum1485230
Variance3081.5874
MonotonicityNot monotonic
2025-10-08T09:48:43.176687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
170 8448
99.3%
862 54
 
0.6%
140 3
 
< 0.1%
480 2
 
< 0.1%
528 1
 
< 0.1%
604 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
140 3
 
< 0.1%
170 8448
99.3%
480 2
 
< 0.1%
528 1
 
< 0.1%
604 1
 
< 0.1%
862 54
 
0.6%
ValueCountFrequency (%)
862 54
 
0.6%
604 1
 
< 0.1%
528 1
 
< 0.1%
480 2
 
< 0.1%
170 8448
99.3%
140 3
 
< 0.1%
10 1
 
< 0.1%

COD_DPTO_R
Real number (ℝ)

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.147121
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:43.286027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q115
median44
Q373
95-th percentile76
Maximum99
Range98
Interquartile range (IQR)58

Descriptive statistics

Standard deviation27.009941
Coefficient of variation (CV)0.6118166
Kurtosis-1.5022672
Mean44.147121
Median Absolute Deviation (MAD)29
Skewness-0.059412242
Sum375692
Variance729.53694
MonotonicityNot monotonic
2025-10-08T09:48:43.429307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
76 1312
15.4%
41 922
10.8%
13 865
10.2%
8 789
 
9.3%
73 559
 
6.6%
68 519
 
6.1%
20 407
 
4.8%
54 377
 
4.4%
5 352
 
4.1%
70 345
 
4.1%
Other values (24) 2063
24.2%
ValueCountFrequency (%)
1 62
 
0.7%
5 352
4.1%
8 789
9.3%
11 50
 
0.6%
13 865
10.2%
15 19
 
0.2%
17 26
 
0.3%
18 110
 
1.3%
19 153
 
1.8%
20 407
4.8%
ValueCountFrequency (%)
99 7
 
0.1%
97 7
 
0.1%
95 15
 
0.2%
94 8
 
0.1%
91 12
 
0.1%
88 5
 
0.1%
86 81
 
1.0%
85 140
 
1.6%
81 58
 
0.7%
76 1312
15.4%

COD_MUN_R
Real number (ℝ)

Distinct398
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.1698
Minimum1
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:43.569931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median144.5
Q3524
95-th percentile834
Maximum980
Range979
Interquartile range (IQR)523

Descriptive statistics

Standard deviation296.97739
Coefficient of variation (CV)1.1074229
Kurtosis-1.0313833
Mean268.1698
Median Absolute Deviation (MAD)143.5
Skewness0.65118921
Sum2282125
Variance88195.568
MonotonicityNot monotonic
2025-10-08T09:48:43.885170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3435
40.4%
758 164
 
1.9%
11 142
 
1.7%
551 141
 
1.7%
520 123
 
1.4%
268 122
 
1.4%
276 86
 
1.0%
547 82
 
1.0%
573 81
 
1.0%
396 71
 
0.8%
Other values (388) 4063
47.7%
ValueCountFrequency (%)
1 3435
40.4%
3 14
 
0.2%
6 43
 
0.5%
10 19
 
0.2%
11 142
 
1.7%
13 21
 
0.2%
15 2
 
< 0.1%
16 22
 
0.3%
20 28
 
0.3%
25 2
 
< 0.1%
ValueCountFrequency (%)
980 10
 
0.1%
895 13
 
0.2%
894 2
 
< 0.1%
893 6
 
0.1%
892 35
0.4%
890 8
 
0.1%
885 12
 
0.1%
878 3
 
< 0.1%
877 3
 
< 0.1%
875 6
 
0.1%

COD_DPTO_N
Real number (ℝ)

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.982256
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:44.182009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q119
median44
Q373
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)54

Descriptive statistics

Standard deviation26.508777
Coefficient of variation (CV)0.58931631
Kurtosis-1.481871
Mean44.982256
Median Absolute Deviation (MAD)26
Skewness-0.11862592
Sum382799
Variance702.71528
MonotonicityNot monotonic
2025-10-08T09:48:44.465913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
76 1387
16.3%
41 1043
12.3%
8 852
10.0%
13 739
8.7%
68 558
 
6.6%
20 494
 
5.8%
73 485
 
5.7%
54 422
 
5.0%
70 414
 
4.9%
5 307
 
3.6%
Other values (23) 1809
21.3%
ValueCountFrequency (%)
5 307
 
3.6%
8 852
10.0%
11 91
 
1.1%
13 739
8.7%
15 19
 
0.2%
17 15
 
0.2%
18 65
 
0.8%
19 49
 
0.6%
20 494
5.8%
23 239
 
2.8%
ValueCountFrequency (%)
99 6
 
0.1%
97 6
 
0.1%
95 5
 
0.1%
94 9
 
0.1%
91 8
 
0.1%
88 1
 
< 0.1%
86 61
 
0.7%
85 143
 
1.7%
81 37
 
0.4%
76 1387
16.3%

COD_MUN_N
Real number (ℝ)

Distinct290
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45095.979
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:44.762747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile8001
Q119431.75
median44430
Q373001
95-th percentile76323.95
Maximum99773
Range94772
Interquartile range (IQR)53569.25

Descriptive statistics

Standard deviation26518.611
Coefficient of variation (CV)0.58804822
Kurtosis-1.4823026
Mean45095.979
Median Absolute Deviation (MAD)25785
Skewness-0.11884638
Sum3.8376678 × 108
Variance7.032367 × 108
MonotonicityNot monotonic
2025-10-08T09:48:45.093483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76001 1142
 
13.4%
41001 753
 
8.8%
8001 680
 
8.0%
13001 659
 
7.7%
70001 389
 
4.6%
54001 353
 
4.1%
20011 240
 
2.8%
50001 238
 
2.8%
68001 237
 
2.8%
73001 237
 
2.8%
Other values (280) 3582
42.1%
ValueCountFrequency (%)
5001 157
1.8%
5030 1
 
< 0.1%
5031 1
 
< 0.1%
5040 1
 
< 0.1%
5045 41
 
0.5%
5051 4
 
< 0.1%
5079 1
 
< 0.1%
5088 9
 
0.1%
5101 5
 
0.1%
5129 4
 
< 0.1%
ValueCountFrequency (%)
99773 1
 
< 0.1%
99524 1
 
< 0.1%
99001 4
< 0.1%
97001 6
0.1%
95025 1
 
< 0.1%
95001 4
< 0.1%
94343 2
 
< 0.1%
94001 7
0.1%
91001 8
0.1%
88001 1
 
< 0.1%

FEC_CON
Date

Distinct1675
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size133.0 KiB
Minimum2019-12-29 00:00:00
Maximum2025-01-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:45.296640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:45.449668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

INI_SIN
Date

Distinct1680
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size133.0 KiB
Minimum2019-12-24 00:00:00
Maximum2024-12-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:45.594990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:45.751200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TIP_CAS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
2
4949 
3
3561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 4949
58.2%
3 3561
41.8%

Length

2025-10-08T09:48:45.891864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:45.988111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4949
58.2%
3 3561
41.8%

Most occurring characters

ValueCountFrequency (%)
2 4949
58.2%
3 3561
41.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4949
58.2%
3 3561
41.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4949
58.2%
3 3561
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4949
58.2%
3 3561
41.8%

PAC_HOS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
8172 
2
 
338

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8172
96.0%
2 338
 
4.0%

Length

2025-10-08T09:48:46.097522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:46.206893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8172
96.0%
2 338
 
4.0%

Most occurring characters

ValueCountFrequency (%)
1 8172
96.0%
2 338
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8172
96.0%
2 338
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8172
96.0%
2 338
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8172
96.0%
2 338
 
4.0%

FEC_HOS
Date

Missing 

Distinct1669
Distinct (%)20.4%
Missing338
Missing (%)4.0%
Memory size133.0 KiB
Minimum2019-12-30 00:00:00
Maximum2025-01-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:46.316236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:46.694076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CON_FIN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
7827 
2
 
683

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7827
92.0%
2 683
 
8.0%

Length

2025-10-08T09:48:47.023484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:47.243514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7827
92.0%
2 683
 
8.0%

Most occurring characters

ValueCountFrequency (%)
1 7827
92.0%
2 683
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7827
92.0%
2 683
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7827
92.0%
2 683
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7827
92.0%
2 683
 
8.0%

FEC_DEF
Date

Missing 

Distinct507
Distinct (%)75.8%
Missing7841
Missing (%)92.1%
Memory size133.0 KiB
Minimum2019-12-31 00:00:00
Maximum2024-12-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:47.493513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:47.823246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AJUSTE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
3
5136 
0
2790 
7
572 
5
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row0
5th row3

Common Values

ValueCountFrequency (%)
3 5136
60.4%
0 2790
32.8%
7 572
 
6.7%
5 12
 
0.1%

Length

2025-10-08T09:48:48.122752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:48.263370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5136
60.4%
0 2790
32.8%
7 572
 
6.7%
5 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 5136
60.4%
0 2790
32.8%
7 572
 
6.7%
5 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 5136
60.4%
0 2790
32.8%
7 572
 
6.7%
5 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 5136
60.4%
0 2790
32.8%
7 572
 
6.7%
5 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 5136
60.4%
0 2790
32.8%
7 572
 
6.7%
5 12
 
0.1%

FECHA_NTO
Date

Missing 

Distinct5111
Distinct (%)75.3%
Missing1720
Missing (%)20.2%
Memory size133.0 KiB
Minimum1923-03-28 00:00:00
Maximum2024-10-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:48.388332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:48.544618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CER_DEF
Real number (ℝ)

Missing 

Distinct629
Distinct (%)92.4%
Missing7829
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean1.5802527 × 1013
Minimum23777730
Maximum2.302522 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:48.687776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23777730
5-th percentile7.2219174 × 108
Q17.2941229 × 108
median2.307562 × 1013
Q32.4053521 × 1013
95-th percentile2.4102621 × 1013
Maximum2.302522 × 1014
Range2.3025218 × 1014
Interquartile range (IQR)2.4052791 × 1013

Descriptive statistics

Standard deviation1.6046624 × 1013
Coefficient of variation (CV)1.0154467
Kurtosis84.397198
Mean1.5802527 × 1013
Median Absolute Deviation (MAD)1.0026003 × 1012
Skewness6.3438108
Sum1.0761521 × 1016
Variance2.5749414 × 1026
MonotonicityNot monotonic
2025-10-08T09:48:48.844026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
729351157 2
 
< 0.1%
2.301292015 × 10132
 
< 0.1%
2.307972031 × 10132
 
< 0.1%
2.301882015 × 10132
 
< 0.1%
2.305192025 × 10132
 
< 0.1%
722673764 2
 
< 0.1%
2.30320202 × 10132
 
< 0.1%
2.307872032 × 10132
 
< 0.1%
2.305122025 × 10132
 
< 0.1%
2.31016204 × 10132
 
< 0.1%
Other values (619) 661
 
7.8%
(Missing) 7829
92.0%
ValueCountFrequency (%)
23777730 1
< 0.1%
31449596 1
< 0.1%
72217297 1
< 0.1%
72275509 1
< 0.1%
72934867 2
< 0.1%
122154581 1
< 0.1%
220833200 1
< 0.1%
220882200 1
< 0.1%
705376789 1
< 0.1%
716013885 2
< 0.1%
ValueCountFrequency (%)
2.302522002 × 10141
< 0.1%
2.210542001 × 10141
< 0.1%
2.412872074 × 10131
< 0.1%
2.412872074 × 10131
< 0.1%
2.412842074 × 10131
< 0.1%
2.412712075 × 10131
< 0.1%
2.412702075 × 10131
< 0.1%
2.412682075 × 10131
< 0.1%
2.412642076 × 10131
< 0.1%
2.412612075 × 10131
< 0.1%

CBMTE
Text

Missing 

Distinct75
Distinct (%)10.9%
Missing7825
Missing (%)92.0%
Memory size351.8 KiB
2025-10-08T09:48:48.984653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9970803
Min length3

Characters and Unicode

Total characters2738
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)5.7%

Sample

1st rowR571
2nd rowG932
3rd rowI411
4th rowJ189
5th rowR578
ValueCountFrequency (%)
a91x 290
42.3%
a90x 85
 
12.4%
r571 56
 
8.2%
r579 45
 
6.6%
r572 25
 
3.6%
r578 24
 
3.5%
r570 19
 
2.8%
j960 10
 
1.5%
r092 9
 
1.3%
i469 9
 
1.3%
Other values (65) 113
 
16.5%
2025-10-08T09:48:49.284057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 536
19.6%
X 392
14.3%
1 389
14.2%
A 386
14.1%
5 191
 
7.0%
R 190
 
6.9%
7 184
 
6.7%
0 144
 
5.3%
8 72
 
2.6%
2 55
 
2.0%
Other values (17) 199
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1672
61.1%
Uppercase Letter 1066
38.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 392
36.8%
A 386
36.2%
R 190
17.8%
I 26
 
2.4%
J 23
 
2.2%
D 16
 
1.5%
K 9
 
0.8%
G 5
 
0.5%
N 5
 
0.5%
B 4
 
0.4%
Other values (7) 10
 
0.9%
Decimal Number
ValueCountFrequency (%)
9 536
32.1%
1 389
23.3%
5 191
 
11.4%
7 184
 
11.0%
0 144
 
8.6%
8 72
 
4.3%
2 55
 
3.3%
6 54
 
3.2%
4 33
 
2.0%
3 14
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1672
61.1%
Latin 1066
38.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 392
36.8%
A 386
36.2%
R 190
17.8%
I 26
 
2.4%
J 23
 
2.2%
D 16
 
1.5%
K 9
 
0.8%
G 5
 
0.5%
N 5
 
0.5%
B 4
 
0.4%
Other values (7) 10
 
0.9%
Common
ValueCountFrequency (%)
9 536
32.1%
1 389
23.3%
5 191
 
11.4%
7 184
 
11.0%
0 144
 
8.6%
8 72
 
4.3%
2 55
 
3.3%
6 54
 
3.2%
4 33
 
2.0%
3 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 536
19.6%
X 392
14.3%
1 389
14.2%
A 386
14.1%
5 191
 
7.0%
R 190
 
6.9%
7 184
 
6.7%
0 144
 
5.3%
8 72
 
2.6%
2 55
 
2.0%
Other values (17) 199
 
7.3%

FEC_ARC_XL
Date

Distinct1109
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size133.0 KiB
Minimum2021-01-06 00:00:00
Maximum2025-04-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:49.487218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:49.659988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FEC_AJU
Date

Distinct1601
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size133.0 KiB
Minimum2019-12-31 00:00:00
Maximum2025-03-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T09:48:49.941751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:50.272601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FM_FUERZA
Categorical

Missing 

Distinct3
Distinct (%)30.0%
Missing8500
Missing (%)99.9%
Memory size598.3 KiB
3.0
4 
4.0
4 
5.0
2 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row4.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 4
 
< 0.1%
4.0 4
 
< 0.1%
5.0 2
 
< 0.1%
(Missing) 8500
99.9%

Length

2025-10-08T09:48:50.585097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:50.791021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 4
40.0%
4.0 4
40.0%
5.0 2
20.0%

Most occurring characters

ValueCountFrequency (%)
. 10
33.3%
0 10
33.3%
3 4
 
13.3%
4 4
 
13.3%
5 2
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
66.7%
Other Punctuation 10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
50.0%
3 4
 
20.0%
4 4
 
20.0%
5 2
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 10
33.3%
0 10
33.3%
3 4
 
13.3%
4 4
 
13.3%
5 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 10
33.3%
0 10
33.3%
3 4
 
13.3%
4 4
 
13.3%
5 2
 
6.7%

FM_UNIDAD
Real number (ℝ)

Missing 

Distinct7
Distinct (%)70.0%
Missing8500
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean2.338127 × 109
Minimum1
Maximum5.449882 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.0 KiB
2025-10-08T09:48:51.009768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19.2514323 × 108
median1.4001815 × 109
Q34.5076925 × 109
95-th percentile5.2474604 × 109
Maximum5.449882 × 109
Range5.449882 × 109
Interquartile range (IQR)3.5825493 × 109

Descriptive statistics

Standard deviation2.1205397 × 109
Coefficient of variation (CV)0.90693948
Kurtosis-1.5060475
Mean2.338127 × 109
Median Absolute Deviation (MAD)1.4001815 × 109
Skewness0.52751534
Sum2.338127 × 1010
Variance4.4966888 × 1018
MonotonicityNot monotonic
2025-10-08T09:48:51.244103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5000056300 2
 
< 0.1%
1 2
 
< 0.1%
1400181500 2
 
< 0.1%
800130632 1
 
< 0.1%
3030601180 1
 
< 0.1%
5449882013 1
 
< 0.1%
1300181039 1
 
< 0.1%
(Missing) 8500
99.9%
ValueCountFrequency (%)
1 2
< 0.1%
800130632 1
< 0.1%
1300181039 1
< 0.1%
1400181500 2
< 0.1%
3030601180 1
< 0.1%
5000056300 2
< 0.1%
5449882013 1
< 0.1%
ValueCountFrequency (%)
5449882013 1
< 0.1%
5000056300 2
< 0.1%
3030601180 1
< 0.1%
1400181500 2
< 0.1%
1300181039 1
< 0.1%
800130632 1
< 0.1%
1 2
< 0.1%

FM_GRADO
Unsupported

Missing  Rejected  Unsupported 

Missing8500
Missing (%)99.9%
Memory size332.6 KiB

confirmados
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
8033 
0
 
477

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8033
94.4%
0 477
 
5.6%

Length

2025-10-08T09:48:51.402977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:51.496727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8033
94.4%
0 477
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 8033
94.4%
0 477
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8033
94.4%
0 477
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8033
94.4%
0 477
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8033
94.4%
0 477
 
5.6%

va_sispro
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
1
8510 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8510
100.0%

Length

2025-10-08T09:48:51.606136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:51.699890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8510
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8510
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8510
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8510
100.0%

Estado_final_de_caso
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
3
8021 
2
 
477
5
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8510
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 8021
94.3%
2 477
 
5.6%
5 12
 
0.1%

Length

2025-10-08T09:48:51.794542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:51.905383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 8021
94.3%
2 477
 
5.6%
5 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 8021
94.3%
2 477
 
5.6%
5 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8510
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8021
94.3%
2 477
 
5.6%
5 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8021
94.3%
2 477
 
5.6%
5 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8021
94.3%
2 477
 
5.6%
5 12
 
0.1%

nom_est_f_caso
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size748.7 KiB
Confirmado por laboratorio
8021 
Probable
 
477
Confirmado por Nexo Epidemiológico
 
12

Length

Max length34
Median length26
Mean length25.00235
Min length8

Characters and Unicode

Total characters212770
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProbable
2nd rowConfirmado por laboratorio
3rd rowConfirmado por laboratorio
4th rowConfirmado por laboratorio
5th rowConfirmado por laboratorio

Common Values

ValueCountFrequency (%)
Confirmado por laboratorio 8021
94.3%
Probable 477
 
5.6%
Confirmado por Nexo Epidemiológico 12
 
0.1%

Length

2025-10-08T09:48:52.014723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:52.139761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
confirmado 8033
32.7%
por 8033
32.7%
laboratorio 8021
32.6%
probable 477
 
1.9%
nexo 12
 
< 0.1%
epidemiológico 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 48675
22.9%
r 32585
15.3%
a 24552
11.5%
i 16090
 
7.6%
16078
 
7.6%
b 8975
 
4.2%
l 8510
 
4.0%
p 8045
 
3.8%
m 8045
 
3.8%
d 8045
 
3.8%
Other values (12) 33170
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 188158
88.4%
Space Separator 16078
 
7.6%
Uppercase Letter 8534
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 48675
25.9%
r 32585
17.3%
a 24552
13.0%
i 16090
 
8.6%
b 8975
 
4.8%
l 8510
 
4.5%
p 8045
 
4.3%
m 8045
 
4.3%
d 8045
 
4.3%
f 8033
 
4.3%
Other values (7) 16603
 
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 8033
94.1%
P 477
 
5.6%
N 12
 
0.1%
E 12
 
0.1%
Space Separator
ValueCountFrequency (%)
16078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196692
92.4%
Common 16078
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 48675
24.7%
r 32585
16.6%
a 24552
12.5%
i 16090
 
8.2%
b 8975
 
4.6%
l 8510
 
4.3%
p 8045
 
4.1%
m 8045
 
4.1%
d 8045
 
4.1%
C 8033
 
4.1%
Other values (11) 25137
12.8%
Common
ValueCountFrequency (%)
16078
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212758
> 99.9%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 48675
22.9%
r 32585
15.3%
a 24552
11.5%
i 16090
 
7.6%
16078
 
7.6%
b 8975
 
4.2%
l 8510
 
4.0%
p 8045
 
3.8%
m 8045
 
3.8%
d 8045
 
3.8%
Other values (11) 33158
15.6%
None
ValueCountFrequency (%)
ó 12
100.0%

Nom_upgd
Text

Distinct702
Distinct (%)8.3%
Missing12
Missing (%)0.1%
Memory size863.3 KiB
2025-10-08T09:48:52.327883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length42
Mean length33.849729
Min length6

Characters and Unicode

Total characters287655
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)2.8%

Sample

1st rowESE HOSPITAL SAN ROQUE
2nd rowFUNDACION CLINICA INFANTIL CLUB NOEL
3rd rowFUNDACION CLINICA INFANTIL CLUB NOEL
4th rowFUNDACION CLINICA INFANTIL CLUB NOEL
5th rowHOSPITAL INTERNACIONAL DE COLOMBIA
ValueCountFrequency (%)
clinica 3453
 
7.9%
hospital 2867
 
6.6%
de 2793
 
6.4%
ese 1436
 
3.3%
sas 1350
 
3.1%
sa 1199
 
2.8%
san 1097
 
2.5%
del 1067
 
2.4%
fundacion 1005
 
2.3%
ips 840
 
1.9%
Other values (954) 26487
60.8%
2025-10-08T09:48:52.703862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 36561
12.7%
35263
12.3%
I 28404
9.9%
E 23304
 
8.1%
L 20192
 
7.0%
S 19827
 
6.9%
N 18842
 
6.6%
O 17113
 
5.9%
C 16631
 
5.8%
D 12995
 
4.5%
Other values (37) 58523
20.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 251528
87.4%
Space Separator 35263
 
12.3%
Other Punctuation 523
 
0.2%
Dash Punctuation 171
 
0.1%
Decimal Number 123
 
< 0.1%
Other Symbol 46
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36561
14.5%
I 28404
11.3%
E 23304
9.3%
L 20192
8.0%
S 19827
7.9%
N 18842
7.5%
O 17113
 
6.8%
C 16631
 
6.6%
D 12995
 
5.2%
R 11173
 
4.4%
Other values (22) 46486
18.5%
Decimal Number
ValueCountFrequency (%)
1 56
45.5%
4 28
22.8%
0 15
 
12.2%
2 8
 
6.5%
3 6
 
4.9%
5 3
 
2.4%
6 3
 
2.4%
9 2
 
1.6%
7 2
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 264
50.5%
& 259
49.5%
Space Separator
ValueCountFrequency (%)
35263
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171
100.0%
Other Symbol
ValueCountFrequency (%)
° 46
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 251528
87.4%
Common 36127
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36561
14.5%
I 28404
11.3%
E 23304
9.3%
L 20192
8.0%
S 19827
7.9%
N 18842
7.5%
O 17113
 
6.8%
C 16631
 
6.6%
D 12995
 
5.2%
R 11173
 
4.4%
Other values (22) 46486
18.5%
Common
ValueCountFrequency (%)
35263
97.6%
. 264
 
0.7%
& 259
 
0.7%
- 171
 
0.5%
1 56
 
0.2%
° 46
 
0.1%
4 28
 
0.1%
0 15
 
< 0.1%
2 8
 
< 0.1%
3 6
 
< 0.1%
Other values (5) 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 286971
99.8%
None 684
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36561
12.7%
35263
12.3%
I 28404
9.9%
E 23304
8.1%
L 20192
 
7.0%
S 19827
 
6.9%
N 18842
 
6.6%
O 17113
 
6.0%
C 16631
 
5.8%
D 12995
 
4.5%
Other values (30) 57839
20.2%
None
ValueCountFrequency (%)
Ñ 595
87.0%
° 46
 
6.7%
Ó 23
 
3.4%
Í 8
 
1.2%
Ú 8
 
1.2%
Ì 3
 
0.4%
É 1
 
0.1%

Pais_ocurrencia
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size606.8 KiB
COLOMBIA
8447 
VENEZUELA
 
60
REPÚBLICA DOMINICANA
 
1
PERÚ
 
1
BOLIVIA
 
1

Length

Max length20
Median length8
Mean length8.0078731
Min length4

Characters and Unicode

Total characters68147
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 8447
99.3%
VENEZUELA 60
 
0.7%
REPÚBLICA DOMINICANA 1
 
< 0.1%
PERÚ 1
 
< 0.1%
BOLIVIA 1
 
< 0.1%

Length

2025-10-08T09:48:53.050427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:53.316011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
colombia 8447
99.2%
venezuela 60
 
0.7%
república 1
 
< 0.1%
dominicana 1
 
< 0.1%
perú 1
 
< 0.1%
bolivia 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 16896
24.8%
A 8511
12.5%
L 8509
12.5%
I 8452
12.4%
C 8449
12.4%
B 8449
12.4%
M 8448
12.4%
E 182
 
0.3%
N 62
 
0.1%
V 61
 
0.1%
Other values (7) 128
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 68146
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 16896
24.8%
A 8511
12.5%
L 8509
12.5%
I 8452
12.4%
C 8449
12.4%
B 8449
12.4%
M 8448
12.4%
E 182
 
0.3%
N 62
 
0.1%
V 61
 
0.1%
Other values (6) 127
 
0.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68146
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 16896
24.8%
A 8511
12.5%
L 8509
12.5%
I 8452
12.4%
C 8449
12.4%
B 8449
12.4%
M 8448
12.4%
E 182
 
0.3%
N 62
 
0.1%
V 61
 
0.1%
Other values (6) 127
 
0.2%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68145
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 16896
24.8%
A 8511
12.5%
L 8509
12.5%
I 8452
12.4%
C 8449
12.4%
B 8449
12.4%
M 8448
12.4%
E 182
 
0.3%
N 62
 
0.1%
V 61
 
0.1%
Other values (6) 126
 
0.2%
None
ValueCountFrequency (%)
Ú 2
100.0%

Nombre_evento
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size645.5 KiB
DENGUE GRAVE
7879 
MORTALIDAD POR DENGUE
 
631

Length

Max length21
Median length12
Mean length12.667333
Min length12

Characters and Unicode

Total characters107799
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDENGUE GRAVE
2nd rowDENGUE GRAVE
3rd rowDENGUE GRAVE
4th rowDENGUE GRAVE
5th rowDENGUE GRAVE

Common Values

ValueCountFrequency (%)
DENGUE GRAVE 7879
92.6%
MORTALIDAD POR DENGUE 631
 
7.4%

Length

2025-10-08T09:48:53.584142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:53.802851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dengue 8510
48.2%
grave 7879
44.6%
mortalidad 631
 
3.6%
por 631
 
3.6%

Most occurring characters

ValueCountFrequency (%)
E 24899
23.1%
G 16389
15.2%
D 9772
 
9.1%
9141
 
8.5%
R 9141
 
8.5%
A 9141
 
8.5%
N 8510
 
7.9%
U 8510
 
7.9%
V 7879
 
7.3%
O 1262
 
1.2%
Other values (5) 3155
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98658
91.5%
Space Separator 9141
 
8.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 24899
25.2%
G 16389
16.6%
D 9772
 
9.9%
R 9141
 
9.3%
A 9141
 
9.3%
N 8510
 
8.6%
U 8510
 
8.6%
V 7879
 
8.0%
O 1262
 
1.3%
M 631
 
0.6%
Other values (4) 2524
 
2.6%
Space Separator
ValueCountFrequency (%)
9141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98658
91.5%
Common 9141
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 24899
25.2%
G 16389
16.6%
D 9772
 
9.9%
R 9141
 
9.3%
A 9141
 
9.3%
N 8510
 
8.6%
U 8510
 
8.6%
V 7879
 
8.0%
O 1262
 
1.3%
M 631
 
0.6%
Other values (4) 2524
 
2.6%
Common
ValueCountFrequency (%)
9141
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 24899
23.1%
G 16389
15.2%
D 9772
 
9.1%
9141
 
8.5%
R 9141
 
8.5%
A 9141
 
8.5%
N 8510
 
7.9%
U 8510
 
7.9%
V 7879
 
7.3%
O 1262
 
1.2%
Other values (5) 3155
 
2.9%

Departamento_ocurrencia
Categorical

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size600.6 KiB
VALLE
1319 
HUILA
922 
BOLIVAR
871 
ATLANTICO
782 
TOLIMA
585 
Other values (28)
4031 

Length

Max length15
Median length12
Mean length7.0116334
Min length4

Characters and Unicode

Total characters59669
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVALLE
2nd rowVALLE
3rd rowVALLE
4th rowVALLE
5th rowSANTANDER

Common Values

ValueCountFrequency (%)
VALLE 1319
15.5%
HUILA 922
10.8%
BOLIVAR 871
10.2%
ATLANTICO 782
 
9.2%
TOLIMA 585
 
6.9%
SANTANDER 521
 
6.1%
CESAR 409
 
4.8%
NORTE SANTANDER 374
 
4.4%
ANTIOQUIA 345
 
4.1%
SUCRE 343
 
4.0%
Other values (23) 2039
24.0%

Length

2025-10-08T09:48:54.071148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valle 1319
14.8%
huila 922
10.4%
santander 895
10.1%
bolivar 871
9.8%
atlantico 782
 
8.8%
tolima 585
 
6.6%
cesar 409
 
4.6%
norte 374
 
4.2%
antioquia 345
 
3.9%
sucre 343
 
3.9%
Other values (24) 2044
23.0%

Most occurring characters

ValueCountFrequency (%)
A 11979
20.1%
L 6127
10.3%
I 4604
 
7.7%
T 4272
 
7.2%
E 4226
 
7.1%
N 4124
 
6.9%
R 4064
 
6.8%
O 3736
 
6.3%
C 2826
 
4.7%
U 2586
 
4.3%
Other values (15) 11125
18.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 59290
99.4%
Space Separator 379
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11979
20.2%
L 6127
10.3%
I 4604
 
7.8%
T 4272
 
7.2%
E 4226
 
7.1%
N 4124
 
7.0%
R 4064
 
6.9%
O 3736
 
6.3%
C 2826
 
4.8%
U 2586
 
4.4%
Other values (14) 10746
18.1%
Space Separator
ValueCountFrequency (%)
379
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59290
99.4%
Common 379
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11979
20.2%
L 6127
10.3%
I 4604
 
7.8%
T 4272
 
7.2%
E 4226
 
7.1%
N 4124
 
7.0%
R 4064
 
6.9%
O 3736
 
6.3%
C 2826
 
4.8%
U 2586
 
4.4%
Other values (14) 10746
18.1%
Common
ValueCountFrequency (%)
379
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59599
99.9%
None 70
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11979
20.1%
L 6127
10.3%
I 4604
 
7.7%
T 4272
 
7.2%
E 4226
 
7.1%
N 4124
 
6.9%
R 4064
 
6.8%
O 3736
 
6.3%
C 2826
 
4.7%
U 2586
 
4.3%
Other values (14) 11055
18.5%
None
ValueCountFrequency (%)
Ñ 70
100.0%

Municipio_ocurrencia
Text

Distinct603
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size614.5 KiB
2025-10-08T09:48:54.399227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length8.5699177
Min length4

Characters and Unicode

Total characters72930
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)1.4%

Sample

1st rowPRADERA
2nd rowCALI
3rd rowCALI
4th rowCALI
5th rowBARRANCABERMEJA
ValueCountFrequency (%)
cali 768
 
7.2%
cartagena 453
 
4.3%
barranquilla 380
 
3.6%
san 354
 
3.3%
neiva 286
 
2.7%
de 239
 
2.2%
la 198
 
1.9%
puerto 192
 
1.8%
cucuta 182
 
1.7%
el 166
 
1.6%
Other values (632) 7433
69.8%
2025-10-08T09:48:54.775835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14374
19.7%
I 5811
 
8.0%
L 5553
 
7.6%
E 5326
 
7.3%
R 5039
 
6.9%
N 4431
 
6.1%
C 4326
 
5.9%
O 4204
 
5.8%
U 3255
 
4.5%
T 2909
 
4.0%
Other values (23) 17702
24.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 70543
96.7%
Space Separator 2141
 
2.9%
Open Punctuation 93
 
0.1%
Close Punctuation 90
 
0.1%
Connector Punctuation 63
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14374
20.4%
I 5811
 
8.2%
L 5553
 
7.9%
E 5326
 
7.6%
R 5039
 
7.1%
N 4431
 
6.3%
C 4326
 
6.1%
O 4204
 
6.0%
U 3255
 
4.6%
T 2909
 
4.1%
Other values (19) 15315
21.7%
Space Separator
ValueCountFrequency (%)
2141
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70543
96.7%
Common 2387
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14374
20.4%
I 5811
 
8.2%
L 5553
 
7.9%
E 5326
 
7.6%
R 5039
 
7.1%
N 4431
 
6.3%
C 4326
 
6.1%
O 4204
 
6.0%
U 3255
 
4.6%
T 2909
 
4.1%
Other values (19) 15315
21.7%
Common
ValueCountFrequency (%)
2141
89.7%
( 93
 
3.9%
) 90
 
3.8%
_ 63
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72831
99.9%
None 99
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14374
19.7%
I 5811
 
8.0%
L 5553
 
7.6%
E 5326
 
7.3%
R 5039
 
6.9%
N 4431
 
6.1%
C 4326
 
5.9%
O 4204
 
5.8%
U 3255
 
4.5%
T 2909
 
4.0%
Other values (19) 17603
24.2%
None
ValueCountFrequency (%)
Ñ 80
80.8%
Ì 15
 
15.2%
É 3
 
3.0%
Ú 1
 
1.0%

Pais_residencia
Categorical

Imbalance 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size607.0 KiB
COLOMBIA
8448 
VENEZUELA
 
54
REPÚBLICA CENTROAFRICANA
 
3
MAURICIO
 
2
PAÍSES BAJOS
 
1
Other values (2)
 
2

Length

Max length24
Median length8
Mean length8.0121034
Min length4

Characters and Unicode

Total characters68183
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 8448
99.3%
VENEZUELA 54
 
0.6%
REPÚBLICA CENTROAFRICANA 3
 
< 0.1%
MAURICIO 2
 
< 0.1%
PAÍSES BAJOS 1
 
< 0.1%
PERÚ 1
 
< 0.1%
ANTÁRTIDA 1
 
< 0.1%

Length

2025-10-08T09:48:54.932126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T09:48:55.059652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
colombia 8448
99.2%
venezuela 54
 
0.6%
república 3
 
< 0.1%
centroafricana 3
 
< 0.1%
mauricio 2
 
< 0.1%
países 1
 
< 0.1%
bajos 1
 
< 0.1%
perú 1
 
< 0.1%
antártida 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 16902
24.8%
A 8520
12.5%
L 8505
12.5%
C 8459
12.4%
I 8459
12.4%
B 8452
12.4%
M 8450
12.4%
E 170
 
0.2%
N 61
 
0.1%
U 56
 
0.1%
Other values (13) 149
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 68179
> 99.9%
Space Separator 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 16902
24.8%
A 8520
12.5%
L 8505
12.5%
C 8459
12.4%
I 8459
12.4%
B 8452
12.4%
M 8450
12.4%
E 170
 
0.2%
N 61
 
0.1%
U 56
 
0.1%
Other values (12) 145
 
0.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68179
> 99.9%
Common 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 16902
24.8%
A 8520
12.5%
L 8505
12.5%
C 8459
12.4%
I 8459
12.4%
B 8452
12.4%
M 8450
12.4%
E 170
 
0.2%
N 61
 
0.1%
U 56
 
0.1%
Other values (12) 145
 
0.2%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68177
> 99.9%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 16902
24.8%
A 8520
12.5%
L 8505
12.5%
C 8459
12.4%
I 8459
12.4%
B 8452
12.4%
M 8450
12.4%
E 170
 
0.2%
N 61
 
0.1%
U 56
 
0.1%
Other values (10) 143
 
0.2%
None
ValueCountFrequency (%)
Ú 4
66.7%
Í 1
 
16.7%
Á 1
 
16.7%

Departamento_residencia
Categorical

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size600.7 KiB
VALLE
1312 
HUILA
922 
BOLIVAR
865 
ATLANTICO
789 
TOLIMA
559 
Other values (29)
4063 

Length

Max length15
Median length12
Mean length7.0148061
Min length4

Characters and Unicode

Total characters59696
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVALLE
2nd rowVALLE
3rd rowVALLE
4th rowVALLE
5th rowSANTANDER

Common Values

ValueCountFrequency (%)
VALLE 1312
15.4%
HUILA 922
10.8%
BOLIVAR 865
10.2%
ATLANTICO 789
 
9.3%
TOLIMA 559
 
6.6%
SANTANDER 519
 
6.1%
CESAR 407
 
4.8%
NORTE SANTANDER 377
 
4.4%
ANTIOQUIA 352
 
4.1%
SUCRE 345
 
4.1%
Other values (24) 2063
24.2%

Length

2025-10-08T09:48:55.215865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valle 1312
14.8%
huila 922
10.4%
santander 896
10.1%
bolivar 865
9.7%
atlantico 789
 
8.9%
tolima 559
 
6.3%
cesar 407
 
4.6%
norte 377
 
4.2%
antioquia 352
 
4.0%
sucre 345
 
3.9%
Other values (25) 2068
23.3%

Most occurring characters

ValueCountFrequency (%)
A 11969
20.0%
L 6081
10.2%
I 4594
 
7.7%
T 4315
 
7.2%
E 4203
 
7.0%
N 4127
 
6.9%
R 4062
 
6.8%
O 3804
 
6.4%
C 2809
 
4.7%
U 2590
 
4.3%
Other values (15) 11142
18.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 59314
99.4%
Space Separator 382
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11969
20.2%
L 6081
10.3%
I 4594
 
7.7%
T 4315
 
7.3%
E 4203
 
7.1%
N 4127
 
7.0%
R 4062
 
6.8%
O 3804
 
6.4%
C 2809
 
4.7%
U 2590
 
4.4%
Other values (14) 10760
18.1%
Space Separator
ValueCountFrequency (%)
382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59314
99.4%
Common 382
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11969
20.2%
L 6081
10.3%
I 4594
 
7.7%
T 4315
 
7.3%
E 4203
 
7.1%
N 4127
 
7.0%
R 4062
 
6.8%
O 3804
 
6.4%
C 2809
 
4.7%
U 2590
 
4.4%
Other values (14) 10760
18.1%
Common
ValueCountFrequency (%)
382
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59626
99.9%
None 70
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11969
20.1%
L 6081
10.2%
I 4594
 
7.7%
T 4315
 
7.2%
E 4203
 
7.0%
N 4127
 
6.9%
R 4062
 
6.8%
O 3804
 
6.4%
C 2809
 
4.7%
U 2590
 
4.3%
Other values (14) 11072
18.6%
None
ValueCountFrequency (%)
Ñ 70
100.0%

Municipio_residencia
Text

Distinct613
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size614.2 KiB
2025-10-08T09:48:55.356538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length8.5132785
Min length4

Characters and Unicode

Total characters72448
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)1.5%

Sample

1st rowPRADERA
2nd rowCALI
3rd rowCALI
4th rowCALI
5th rowBARRANCABERMEJA
ValueCountFrequency (%)
cali 779
 
7.4%
cartagena 461
 
4.4%
barranquilla 382
 
3.6%
san 339
 
3.2%
neiva 300
 
2.8%
de 227
 
2.1%
la 193
 
1.8%
cucuta 185
 
1.7%
puerto 185
 
1.7%
soledad 164
 
1.6%
Other values (643) 7363
69.6%
2025-10-08T09:48:55.765380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14278
19.7%
I 5771
 
8.0%
L 5520
 
7.6%
E 5304
 
7.3%
R 4979
 
6.9%
N 4382
 
6.0%
C 4323
 
6.0%
O 4241
 
5.9%
U 3200
 
4.4%
T 2920
 
4.0%
Other values (25) 17530
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 70139
96.8%
Space Separator 2068
 
2.9%
Open Punctuation 91
 
0.1%
Close Punctuation 88
 
0.1%
Connector Punctuation 62
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14278
20.4%
I 5771
 
8.2%
L 5520
 
7.9%
E 5304
 
7.6%
R 4979
 
7.1%
N 4382
 
6.2%
C 4323
 
6.2%
O 4241
 
6.0%
U 3200
 
4.6%
T 2920
 
4.2%
Other values (21) 15221
21.7%
Space Separator
ValueCountFrequency (%)
2068
100.0%
Open Punctuation
ValueCountFrequency (%)
( 91
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70139
96.8%
Common 2309
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14278
20.4%
I 5771
 
8.2%
L 5520
 
7.9%
E 5304
 
7.6%
R 4979
 
7.1%
N 4382
 
6.2%
C 4323
 
6.2%
O 4241
 
6.0%
U 3200
 
4.6%
T 2920
 
4.2%
Other values (21) 15221
21.7%
Common
ValueCountFrequency (%)
2068
89.6%
( 91
 
3.9%
) 88
 
3.8%
_ 62
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72347
99.9%
None 101
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14278
19.7%
I 5771
 
8.0%
L 5520
 
7.6%
E 5304
 
7.3%
R 4979
 
6.9%
N 4382
 
6.1%
C 4323
 
6.0%
O 4241
 
5.9%
U 3200
 
4.4%
T 2920
 
4.0%
Other values (19) 17429
24.1%
None
ValueCountFrequency (%)
Ñ 79
78.2%
Ì 15
 
14.9%
Ú 3
 
3.0%
É 2
 
2.0%
Á 1
 
1.0%
Í 1
 
1.0%

Departamento_Notificacion
Categorical

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size600.7 KiB
VALLE
1387 
HUILA
1043 
ATLANTICO
852 
BOLIVAR
739 
SANTANDER
558 
Other values (28)
3931 

Length

Max length15
Median length12
Mean length6.9984724
Min length4

Characters and Unicode

Total characters59557
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowVALLE
2nd rowVALLE
3rd rowVALLE
4th rowVALLE
5th rowSANTANDER

Common Values

ValueCountFrequency (%)
VALLE 1387
16.3%
HUILA 1043
12.3%
ATLANTICO 852
10.0%
BOLIVAR 739
8.7%
SANTANDER 558
 
6.6%
CESAR 494
 
5.8%
TOLIMA 485
 
5.7%
NORTE SANTANDER 422
 
5.0%
SUCRE 414
 
4.9%
ANTIOQUIA 307
 
3.6%
Other values (23) 1809
21.3%

Length

2025-10-08T09:48:56.096235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valle 1387
15.5%
huila 1043
11.7%
santander 980
11.0%
atlantico 852
9.5%
bolivar 739
8.3%
cesar 494
 
5.5%
tolima 485
 
5.4%
norte 422
 
4.7%
sucre 414
 
4.6%
antioquia 307
 
3.4%
Other values (24) 1810
20.3%

Most occurring characters

ValueCountFrequency (%)
A 11769
19.8%
L 6180
10.4%
I 4393
 
7.4%
T 4387
 
7.4%
E 4361
 
7.3%
N 4311
 
7.2%
R 4111
 
6.9%
O 3729
 
6.3%
C 2750
 
4.6%
U 2466
 
4.1%
Other values (14) 11100
18.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 59134
99.3%
Space Separator 423
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11769
19.9%
L 6180
10.5%
I 4393
 
7.4%
T 4387
 
7.4%
E 4361
 
7.4%
N 4311
 
7.3%
R 4111
 
7.0%
O 3729
 
6.3%
C 2750
 
4.7%
U 2466
 
4.2%
Other values (13) 10677
18.1%
Space Separator
ValueCountFrequency (%)
423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59134
99.3%
Common 423
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11769
19.9%
L 6180
10.5%
I 4393
 
7.4%
T 4387
 
7.4%
E 4361
 
7.4%
N 4311
 
7.3%
R 4111
 
7.0%
O 3729
 
6.3%
C 2750
 
4.7%
U 2466
 
4.2%
Other values (13) 10677
18.1%
Common
ValueCountFrequency (%)
423
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59482
99.9%
None 75
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11769
19.8%
L 6180
10.4%
I 4393
 
7.4%
T 4387
 
7.4%
E 4361
 
7.3%
N 4311
 
7.2%
R 4111
 
6.9%
O 3729
 
6.3%
C 2750
 
4.6%
U 2466
 
4.1%
Other values (13) 11025
18.5%
None
ValueCountFrequency (%)
Ñ 75
100.0%

Municipio_notificacion
Text

Distinct287
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size608.5 KiB
2025-10-08T09:48:56.533733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length7.940423
Min length4

Characters and Unicode

Total characters67573
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)1.3%

Sample

1st rowPRADERA
2nd rowCALI
3rd rowCALI
4th rowCALI
5th rowPIEDECUESTA
ValueCountFrequency (%)
cali 1142
 
12.5%
neiva 753
 
8.2%
barranquilla 680
 
7.4%
cartagena 659
 
7.2%
sincelejo 389
 
4.3%
cucuta 353
 
3.9%
aguachica 240
 
2.6%
villavicencio 238
 
2.6%
bucaramanga 237
 
2.6%
ibague 237
 
2.6%
Other values (307) 4223
46.1%
2025-10-08T09:48:57.397239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14347
21.2%
I 6334
9.4%
L 5741
8.5%
C 5040
 
7.5%
E 4924
 
7.3%
N 4635
 
6.9%
R 4416
 
6.5%
U 3096
 
4.6%
O 2751
 
4.1%
T 2588
 
3.8%
Other values (17) 13701
20.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66882
99.0%
Space Separator 641
 
0.9%
Open Punctuation 26
 
< 0.1%
Close Punctuation 24
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14347
21.5%
I 6334
9.5%
L 5741
8.6%
C 5040
 
7.5%
E 4924
 
7.4%
N 4635
 
6.9%
R 4416
 
6.6%
U 3096
 
4.6%
O 2751
 
4.1%
T 2588
 
3.9%
Other values (14) 13010
19.5%
Space Separator
ValueCountFrequency (%)
641
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66882
99.0%
Common 691
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14347
21.5%
I 6334
9.5%
L 5741
8.6%
C 5040
 
7.5%
E 4924
 
7.4%
N 4635
 
6.9%
R 4416
 
6.6%
U 3096
 
4.6%
O 2751
 
4.1%
T 2588
 
3.9%
Other values (14) 13010
19.5%
Common
ValueCountFrequency (%)
641
92.8%
( 26
 
3.8%
) 24
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67499
99.9%
None 74
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14347
21.3%
I 6334
9.4%
L 5741
8.5%
C 5040
 
7.5%
E 4924
 
7.3%
N 4635
 
6.9%
R 4416
 
6.5%
U 3096
 
4.6%
O 2751
 
4.1%
T 2588
 
3.8%
Other values (16) 13627
20.2%
None
ValueCountFrequency (%)
Ñ 74
100.0%

Interactions

2025-10-08T09:48:27.237098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:51.433642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:53.988175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-10-08T09:48:00.706196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:03.512989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.009456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:08.322485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:10.229029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.760918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.736741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:17.604288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:20.179630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:22.734931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:25.242333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:27.426836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:51.545999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-10-08T09:48:06.199044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:08.753099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-10-08T09:48:18.034009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-10-08T09:47:56.770110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-10-08T09:48:20.545040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.032532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:25.643880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.007013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:51.851459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:54.419884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:56.889679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:59.099515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:01.344450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:03.925992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.466167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:08.963479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:11.021765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:13.376165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:15.710762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:18.461674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:20.659354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.131718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:25.739125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.209908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:51.968170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:54.520668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.053442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:59.380782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:01.594769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:04.051999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.587704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.068535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:11.259295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:13.494903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:15.967760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:18.715980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:20.764624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.254367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:25.852293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.403913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:52.168088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:54.640020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.157543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:59.600865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:01.829671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:04.167645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.683007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.191988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:11.470356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:13.611272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:16.208406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:18.935696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:20.920095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.365177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:25.983654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.566513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:52.376394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:54.741836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.265444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:59.759826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:02.048163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:04.371524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.802128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.285371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:11.679014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:13.707567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:16.449712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.147343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:21.119028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.478087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.118055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.688694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:52.584758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:54.838646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.368416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:59.865094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:02.302752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:04.601094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.904526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.398515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:11.893919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:13.823287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:16.629650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.352652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:21.364616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.578925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.240306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.781325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:52.787219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:54.949384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.470577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:59.980503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:02.533585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:04.804753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:06.999912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.524144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:11.987920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:13.946452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:16.757416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.465464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:21.570520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:23.686244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.335815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.886430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:52.987244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:55.049581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.567715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:00.096717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:02.707326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:05.011703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:07.111434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.619136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.107762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.040120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:16.876135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.562377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:21.767919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:24.008607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.456038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:28.978573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:53.193591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:55.191893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.656090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:00.197299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:02.829352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:05.218544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:07.287076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.730864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.204979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.178966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:16.993009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.679191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:21.983120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:24.217885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.556622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:29.073159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:53.525128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:55.405352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.766603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:00.293178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:02.929110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:05.416464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:07.502736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.840244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.322185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.307328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:17.109764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.787913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:22.221672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:24.418342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.668519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:29.187871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:53.668384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:55.611567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.867142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:00.403867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:03.208705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:05.634534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:07.707945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:09.943563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.442595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.428788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:17.246288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.886073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:22.422888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:24.624891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.776403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:29.280371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:53.767777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:55.813209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:57.970865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:00.510668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:03.302441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:05.808410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:07.925573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:10.040375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.557089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.524039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:17.369287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:19.984709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:22.529883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:24.820371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:26.873447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:29.371271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:53.885847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:56.013841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:47:58.067196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:00.610274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:03.417250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:05.919644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:08.125080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:10.140944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:12.655397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:14.639746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:17.489288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:20.083768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:22.638406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:25.014715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-08T09:48:27.005034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2025-10-08T09:48:29.663383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-08T09:48:30.315005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-08T09:48:31.280756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

COD_EVEFEC_NOTSEMANAANOCOD_PRECOD_SUBEDADUNI_MEDnacionalidadnombre_nacionalidadSEXOCOD_PAIS_OCOD_DPTO_OCOD_MUN_OAREAOCUPACIONTIP_SSCOD_ASEPER_ETNGRU_POBnom_grupoestratoGP_DISCAPAGP_DESPLAZGP_MIGRANTGP_CARCELAGP_GESTANsem_gesGP_INDIGENGP_POBICFBGP_MAD_COMGP_DESMOVIGP_PSIQUIAGP_VIC_VIOGP_OTROSfuenteCOD_PAIS_RCOD_DPTO_RCOD_MUN_RCOD_DPTO_NCOD_MUN_NFEC_CONINI_SINTIP_CASPAC_HOSFEC_HOSCON_FINFEC_DEFAJUSTEFECHA_NTOCER_DEFCBMTEFEC_ARC_XLFEC_AJUFM_FUERZAFM_UNIDADFM_GRADOconfirmadosva_sisproEstado_final_de_casonom_est_f_casoNom_upgdPais_ocurrenciaNombre_eventoDepartamento_ocurrenciaMunicipio_ocurrenciaPais_residenciaDepartamento_residenciaMunicipio_residenciaDepartamento_NotificacionMunicipio_notificacion
02202020-08-2634202076563040821151170COLOMBIAF1707656319999.0SEPSS186NaN122222222222111707656376765632020-08-262020-08-2222NaN1NaN02005-06-28NaNNaN2021-04-302020-08-31NaNNaNNaN012ProbableESE HOSPITAL SAN ROQUECOLOMBIADENGUE GRAVEVALLEPRADERACOLOMBIAVALLEPRADERAVALLEPRADERA
12202020-04-2617202076001025411101170COLOMBIAF17076119997.0SESS1186NaN3222222222221117076176760012020-04-232020-04-21312020-04-231NaN32010-03-20NaNNaN2021-04-302020-04-27NaNNaNNaN113Confirmado por laboratorioFUNDACION CLINICA INFANTIL CLUB NOELCOLOMBIADENGUE GRAVEVALLECALICOLOMBIAVALLECALIVALLECALI
22202020-04-0314202076001025411111170COLOMBIAF17076119997.0SESS1186NaN1222222222221117076176760012020-04-022020-04-02312020-04-021NaN32008-12-18NaNNaN2021-04-302020-04-18NaNNaNNaN113Confirmado por laboratorioFUNDACION CLINICA INFANTIL CLUB NOELCOLOMBIADENGUE GRAVEVALLECALICOLOMBIAVALLECALIVALLECALI
32202020-03-10920207600102541181170COLOMBIAF17076119997.0CEPS0166NaN2222222222221117076176760012020-03-042020-02-28312020-03-041NaN02011-03-16NaNNaN2021-04-302020-03-10NaNNaNNaN113Confirmado por laboratorioFUNDACION CLINICA INFANTIL CLUB NOELCOLOMBIADENGUE GRAVEVALLECALICOLOMBIAVALLECALIVALLECALI
42202020-03-029202068547049471131170COLOMBIAF170688119997.0PRES0026NaN32222222222211170688168685472020-02-262020-02-25212020-02-281NaN32007-01-23NaNNaN2021-04-302020-03-17NaNNaNNaN113Confirmado por laboratorioHOSPITAL INTERNACIONAL DE COLOMBIACOLOMBIADENGUE GRAVESANTANDERBARRANCABERMEJACOLOMBIASANTANDERBARRANCABERMEJASANTANDERPIEDECUESTA
52202020-05-2721202070001010491101170COLOMBIAF1707067029999.0SESS0246NaN122222222222111707067070700012020-05-272020-05-22212020-05-271NaN32009-12-24NaNNaN2021-04-302020-06-16NaNNaNNaN113Confirmado por laboratorioCLINICA PEDIATRICA NIÑO JESUS LTDACOLOMBIADENGUE GRAVESUCRESAMPUESCOLOMBIASUCRESAMPUESSUCRESINCELEJO
62202020-02-03520207600102870121170COLOMBIAM1707689219999.0CEPS0376NaN22222222222111707689276760012020-02-022020-01-28312020-02-021NaN02017-04-17NaNNaN2021-04-302020-02-03NaNNaNNaN113Confirmado por laboratorioFUNDACION VALLE DEL LILICOLOMBIADENGUE GRAVEVALLEYUMBOCOLOMBIAVALLEYUMBOVALLECALI
72202020-05-0518202076001030661361170COLOMBIAF17076114311.0CEPS0106NaN3222222222221117076176760012020-04-302020-04-27312020-04-301NaN31983-11-14NaNNaN2021-04-302020-07-15NaNNaNNaN113Confirmado por laboratorioCLINICA DE OCCIDENTE SACOLOMBIADENGUE GRAVEVALLECALICOLOMBIAVALLECALIVALLECALI
82202020-03-27122020760010003734261170COLOMBIAF17076114415.0CEPS0186NaN3222222222221117076176760012020-03-232020-03-19212020-03-231NaN31993-07-10NaNNaN2021-04-302020-04-02NaNNaNNaN113Confirmado por laboratorioCLINICA AMIGACOLOMBIADENGUE GRAVEVALLECALICOLOMBIAVALLECALIVALLECALI
92202020-11-1146202041298004191491862VENEZUELAF1704130619996.0NNaN6NaN122122222222131704130641412982020-11-092020-11-09212020-11-091NaN31971-03-19NaNNaN2021-04-302021-01-28NaNNaNNaN113Confirmado por laboratorioHOSPITAL DEPARTAMENTAL SAN VICENTE DE PAULCOLOMBIADENGUE GRAVEHUILAGIGANTECOLOMBIAHUILAGIGANTEHUILAGARZON
COD_EVEFEC_NOTSEMANAANOCOD_PRECOD_SUBEDADUNI_MEDnacionalidadnombre_nacionalidadSEXOCOD_PAIS_OCOD_DPTO_OCOD_MUN_OAREAOCUPACIONTIP_SSCOD_ASEPER_ETNGRU_POBnom_grupoestratoGP_DISCAPAGP_DESPLAZGP_MIGRANTGP_CARCELAGP_GESTANsem_gesGP_INDIGENGP_POBICFBGP_MAD_COMGP_DESMOVIGP_PSIQUIAGP_VIC_VIOGP_OTROSfuenteCOD_PAIS_RCOD_DPTO_RCOD_MUN_RCOD_DPTO_NCOD_MUN_NFEC_CONINI_SINTIP_CASPAC_HOSFEC_HOSCON_FINFEC_DEFAJUSTEFECHA_NTOCER_DEFCBMTEFEC_ARC_XLFEC_AJUFM_FUERZAFM_UNIDADFM_GRADOconfirmadosva_sisproEstado_final_de_casonom_est_f_casoNom_upgdPais_ocurrenciaNombre_eventoDepartamento_ocurrenciaMunicipio_ocurrenciaPais_residenciaDepartamento_residenciaMunicipio_residenciaDepartamento_NotificacionMunicipio_notificacion
2745802024-12-02492024253070020816771170COLOMBIAM17025612199999.03CEPS0176NaN422222222222111702561225253072024-12-012024-11-28212024-12-0122024-12-0231947-11-282.412872e+13R5722025-01-292025-01-29NaNNaNNaN113Confirmado por laboratorioCLINICA COLSUBSIDIO GIRARDOTCOLOMBIAMORTALIDAD POR DENGUECUNDINAMARCARICAURTECOLOMBIACUNDINAMARCARICAURTECUNDINAMARCAGIRARDOT
2755802024-07-0527202463001011331751170COLOMBIAM17063130183240.00SESS0626NaN222222222222111706313063630012024-07-052024-07-01212024-07-0522024-07-0531949-07-032.407892e+13R5722024-10-172024-10-17NaNNaNNaN113Confirmado por laboratorioCLINICA DEL CAFE DUMIAN MEDICAL SASCOLOMBIAMORTALIDAD POR DENGUEQUINDIOCALARCACOLOMBIAQUINDIOCALARCAQUINDIOARMENIA
2765802024-04-0114202450001003211521170COLOMBIAF170501199999.01SEPSS346NaN3222222222221117050150500012024-03-312024-03-25212024-03-3122024-04-0131972-03-272.404142e+13A91X2024-05-022024-05-02NaNNaNNaN113Confirmado por laboratorioINVERSIONES CLINICA DEL META SACOLOMBIAMORTALIDAD POR DENGUEMETAVILLAVICENCIOCOLOMBIAMETAVILLAVICENCIOMETAVILLAVICENCIO
2775802024-05-0719202468276016661761170COLOMBIAM17068679399999.03CEPS0376NaN222222222222111706867968682762024-05-072024-04-29212024-05-0722024-05-0731948-05-072.405332e+13R5702024-06-072024-06-07NaNNaNNaN113Confirmado por laboratorioFUNDACION OFTALMOLOGICA DE SDER FOSCALCOLOMBIAMORTALIDAD POR DENGUESANTANDERSAN GILCOLOMBIASANTANDERSAN GILSANTANDERFLORIDABLANCA
2785802024-09-1237202468679012461841170COLOMBIAF17068679199999.01SEPSS416NaN322222222222111706867968686792024-09-102024-09-05212024-09-1022024-09-1231940-09-042.409712e+13A90X2024-09-122024-09-12NaNNaNNaN113Confirmado por laboratorioCLINICA SANTA CRUZ DE LA LOMACOLOMBIAMORTALIDAD POR DENGUESANTANDERSAN GILCOLOMBIASANTANDERSAN GILSANTANDERSAN GIL
2795802024-05-1220202476520124921461170COLOMBIAF17076520196220.00CEPS0126NaN222222222222111707652076765202024-05-102024-05-05212024-05-1022024-05-1231977-12-012.405632e+13R5782025-03-212025-03-21NaNNaNNaN113Confirmado por laboratorioGYO MEDICAL IPS - PALMIRACOLOMBIAMORTALIDAD POR DENGUEVALLEPALMIRACOLOMBIAVALLEPALMIRAVALLEPALMIRA
2805802024-08-153320245001046482871170COLOMBIAF1705579299999.04SEPSS406NaN222222222222111705579550012024-08-072024-08-07212024-08-0722024-08-1531937-02-152.408752e+13A90X2024-08-152024-08-15NaNNaNNaN113Confirmado por laboratorioCLINICA DEL PRADO SACOLOMBIAMORTALIDAD POR DENGUEANTIOQUIAPUERTO BERRIOCOLOMBIAANTIOQUIAPUERTO BERRIOANTIOQUIAMEDELLIN
2815802024-06-0523202441001005721881170COLOMBIAF1704126399999.01SEPSS056NaN122222222222111704131941410012024-05-272024-05-27212024-05-2722024-06-0431935-06-082.406672e+13R5712024-07-092024-07-09NaNNaNNaN113Confirmado por laboratorioCLINICA UROS SACOLOMBIAMORTALIDAD POR DENGUEHUILAALTAMIRACOLOMBIAHUILAGUADALUPEHUILANEIVA
2825802024-04-3018202473001009441381170COLOMBIAF17073226399999.04SESS0626NaN222222222222111707322673730012024-04-222024-04-20212024-04-2222024-04-3031985-06-182.405772e+13A91X2024-07-102024-07-10NaNNaNNaN113Confirmado por laboratorioCLINICA IBAGUE SACOLOMBIAMORTALIDAD POR DENGUETOLIMACUNDAYCOLOMBIATOLIMACUNDAYTOLIMAIBAGUE
2835802024-04-1315202476001011251571170COLOMBIAF17019212399999.04SEPSI036NaN122222222222111701921276760012024-04-102024-04-07212024-04-1022024-04-1231966-10-232.404812e+13R5782024-04-132024-04-13NaNNaNNaN113Confirmado por laboratorioCLINICA NUESTRA SEÑORA DE LOS REMEDIOSCOLOMBIAMORTALIDAD POR DENGUECAUCACORINTOCOLOMBIACAUCACORINTOVALLECALI

Duplicate rows

Most frequently occurring

COD_EVEFEC_NOTSEMANAANOCOD_PRECOD_SUBEDADUNI_MEDnacionalidadnombre_nacionalidadSEXOCOD_PAIS_OCOD_DPTO_OCOD_MUN_OAREAOCUPACIONTIP_SSCOD_ASEPER_ETNnom_grupoestratoGP_DISCAPAGP_DESPLAZGP_MIGRANTGP_CARCELAGP_GESTANsem_gesGP_INDIGENGP_POBICFBGP_MAD_COMGP_DESMOVIGP_PSIQUIAGP_VIC_VIOGP_OTROSfuenteCOD_PAIS_RCOD_DPTO_RCOD_MUN_RCOD_DPTO_NCOD_MUN_NFEC_CONINI_SINTIP_CASPAC_HOSFEC_HOSCON_FINFEC_DEFAJUSTEFECHA_NTOCER_DEFCBMTEFEC_ARC_XLFEC_AJUFM_FUERZAFM_UNIDADconfirmadosva_sisproEstado_final_de_casonom_est_f_casoNom_upgdPais_ocurrenciaNombre_eventoDepartamento_ocurrenciaMunicipio_ocurrenciaPais_residenciaDepartamento_residenciaMunicipio_residenciaDepartamento_NotificacionMunicipio_notificacion# duplicates
02202022-08-093120227326800794181170COLOMBIAF1707326819997.0SEPSS086222222222222111707326873732682022-08-082022-08-05212022-08-081NaN32013-09-09NaNNaN2022-09-072022-08-30NaNNaN113Confirmado por laboratorioHOSPITAL SAN RAFAEL EMPRESA SOCIAL DEL ESTADOCOLOMBIADENGUE GRAVETOLIMAESPINALCOLOMBIATOLIMAESPINALTOLIMAESPINAL2